title: Using confidence intervals to compare several correlated areas under the receiver operating characteristic curves.
abstract: The performance of a diagnostic tool yielding quantitative or ordinal measurements is often assessed in terms of its area under the receiver operating characteristic curve (AUC). As new diagnostic tools are constantly being developed, a frequently occurring task is to compare multiple AUCs as derived from the same group of subjects. For this purpose, previous methods have usually used an omnibus chi-square test, which may not be very informative. We present here methods for comparing several correlated AUCs using simultaneous confidence intervals. To improve small sample properties, we adopt the method of variance estimates recovery in which confidence limits for each AUC are obtained on the basis of the logit and inverse hyperbolic sine transformations. A simulation study demonstrates the superior performance of the proposed approach. The methods are illustrated with two examples.
authors.affiliation: Department of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, University of Western Ontario, London, Ontario, Canada; Robarts Clinical Trials of Robarts Research Institute, Schulich School of Medicine & Dentistry, University of Western Ontario, London, Ontario, Canada.
authors.forename: G Y
authors.initials: GY
authors.lastname: Zou
authors.forename: Li
authors.initials: L
authors.lastname: Yue
mesh_terms: D019540:Area Under Curve; D002648:Child; D003198:Computer Simulation; D016001:Confidence Intervals; D003955:Diagnostic Tests, Routine / Q000379:methods* / Q000592:standards; D005260:Female; D006801:Humans; D007415:Intestinal Obstruction / Q000150:complications / Q000601:surgery; D009216:Myopia / Q000209:etiology; D009752:Nutritional Status; D010051:Ovarian Neoplasms / Q000150:complications / Q000601:surgery; D012372:ROC Curve
keywords: U-statistic; all pairwise comparisons; comparisons with a standard; diagnostic test; hypothesis testing; nonparametric; simultaneous confidence intervals